Makes sense. How much were the standard errors of your estimates reduced by inclusion of the auxiliary variable?
On Tue, Apr 16, 2013 at 7:35 AM, Trivellore Raghunathan <tera...@umich.edu>wrote: > I agree. In one of our clinical studies, data based on blood work was > missing because of some technical and informed consent isues. But we had > collected a auxiliary variable using dietary questionnaire. It was a good > predictor of blood-work based variable (r=0.6). Through multiple > imputation we were able to reduce the fraction of missing information > considerably. Often, people think of missing data after the data collection > is over. I think we need to think of potential missing data before the data > collection and try to collect auxiliary variables that are predictive of > variables that are likely to have missing values. > > Raghu > > On Mon, Apr 15, 2013 at 8:21 PM, David Judkins <david_judk...@abtassoc.com > > wrote: > >> I would say that it all depends. In Hunsicker's example, peak PRA >> sounds like it was excluded from the outcome space because of colinearity >> issues. This makes it an ideal adjunct variable to the imputation process. >> >> --Dave Judkins >> >> Sent from my iPhone >> >> On Apr 15, 2013, at 7:13 PM, "Paul von Hippel" <paulvonhip...@yahoo.com> >> wrote: >> >> Let me correct my first sentence: What I meant to say is that Meng >> showed that MI imputation is still valid of auxiliary variables have been >> included in the imputation model. So it's a legitimate practice and, if >> its' not too much trouble, why not. But it probably won't make much >> difference. >> >> >> ------------------------------ >> *From:* Paul von Hippel <paulvonhippel.utaus...@gmail.com> >> *To:* IMPUTE@LISTSERV.IT.NORTHWESTERN.EDU >> *Sent:* Monday, April 15, 2013 4:39 PM >> *Subject:* Re: "Accessory" variables in imputation >> >> Meng showed that MI imputation is still valid if auxiliary variables >> have been included in the analysis. In theory auxiliary variables can >> improve the estimates, but in practice they rarely help much. See the >> recent paper by Sarah Mustillo in Sociological Methods & Research. >> >> >> On Mon, Apr 15, 2013 at 4:27 PM, Hunsicker, Lawrence < >> lawrence-hunsic...@uiowa.edu> wrote: >> >> Good afternoon, all: >> >> A question about the use of "accessory" variables in imputation. >> Consider for a moment a kidney transplant survival model in which one has >> data (among other things) on peak panel reactive antibody (peak PRA) and >> the PRA at the time of the actual transplant (current PRA). These actually >> measure different things, but they are obviously strongly correlated. Data >> are missing of some fraction of these covariates, but most of the time one >> or the other is available. Current PRA is considered to be the stronger >> predictor of transplant outcomes. One is developing a model in which one >> wants to limit the model df. So it has been decided that the final model >> will include current PRA but not peak PRA. >> >> I understand that the imputation model must include the outcome variable >> and also all of the covariates that will be used in the final analysis >> model. The question is whether one can/should include additional >> covariates (such as peak PRA) in the imputation model that WON'T be in the >> final analysis model. It would seem that inclusion of peak PRA in the >> imputation model might improve considerably the prediction of current PRA, >> the covariate that will be included in the final analysis model. >> >> Is this legitimate? >> >> Thanks in advance to any guidance from the listserv members. >> >> Larry Hunsicker >> Prof. Internal Medicine >> U. Iowa College of Medicine >> >> >> ________________________________ >> Notice: This UI Health Care e-mail (including attachments) is covered by >> the Electronic Communications Privacy Act, 18 U.S.C. 2510-2521, is >> confidential and may be legally privileged. If you are not the intended >> recipient, you are hereby notified that any retention, dissemination, >> distribution, or copying of this communication is strictly prohibited. >> Please reply to the sender that you have received the message in error, >> then delete it. Thank you. >> ________________________________ >> >> >> >> >> -- >> Best wishes, >> Paul von Hippel >> Assistant Professor >> LBJ School of Public Affairs >> Sid Richardson Hall 3.251 >> University of Texas, Austin >> 2315 Red River, Box Y >> Austin, TX 78712 >> (512) 537-8112 >> >> >> >> >> ------------------------------ >> This message may contain privileged and confidential information intended >> solely for the addressee. Please do not read, disseminate or copy it unless >> you are the intended recipient. If this message has been received in error, >> we kindly ask that you notify the sender immediately by return email and >> delete all copies of the message from your system. >> > > > > -- > Trivellore Raghunathan (Raghu) > Chair and Professor of Biostatistics > School of Public Health > Room M4208 > 1415 Washington Heights > University of Michigan > Ann Arbor, MI 48109 > > Phone: (734) 615-9832 > Fax: (734) 615-7068 > > "A good life is filled with selfless actions full of compassion knowing > well that we are all one" > -- Best wishes, Paul von Hippel Assistant Professor LBJ School of Public Affairs Sid Richardson Hall 3.251 University of Texas, Austin 2315 Red River, Box Y Austin, TX 78712 (512) 537-8112
